A* CCG Parsing with a Supertag and Dependency Factored Model
By Masashi Yoshikawa, Hiroshi Noji, Yuji Matsumoto. 2017
69 citations recorded by the source corpus.
We propose a new A* CCG parsing model in which the probability of a tree is decomposed into factors of CCG categories and its syntactic dependencies both defined on bi-directional LSTMs. Our factored model allows the precomputation of all probabilities and runs very efficiently, while modeling sentence structures explicitly via dependencies. Our model achieves the state-of-the-art results on English and Japanese CCG parsing.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Lasha Abzianidze
- Satoshi Sekine
- Kentaro Inui
- Johan Bos
- Sebastian Riedel
- Peter Schüller
- Phil Blunsom
- Luke S. Zettlemoyer
Citations in the corpus
- The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives (2019, neutral): “Previous work has made some interesting observations regarding these representations; for example, Zhang and Bowman (2018) show that untrained LSTMs outperform trained ones on a word identity prediction task; and Blevins et al.”
- Can Neural Networks Understand Monotonicity Reasoning? (2019, methodology): “To get a whole CCG derivation tree, we parse each sentence by the state-of-the-art CCG parser, de-pccg (Yoshikawa et al., 2017).”